jamie wasilenko, ph.d. · jamie wasilenko, ph.d. eastern laboratory microbiology characterization...

Jamie Wasilenko, Ph.D.Eastern Laboratory Microbiology Characterization Branch

FSIS USDA Office of Public Health Science

USDA, Food Safety and Inspection Service

Whole Genome Sequencing at FSIS: Pipelines and Informatics

May 22, 2017

Food Safety and Inspection Service

Whole Genome Sequencing (WGS) in FSIS- Overview

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Whole Genome Sequencing Methodology in FSIS WGS highlights

Current status

Analytical tools

Application hqSNP analysis

AMR

Harborage

Concluding remarks


FSIS Organizational Structure

Office of the Administrator

Office of Data Integration and Food

Protection (ODIFP)

Office of Field Operations

(OFO)

Office of Outreach, Employee

Education and Training (OOEET)

Office of Public Affairs

and Consumer Education (OPACE)

Office of Management

(OM)

Office of Investigation, Enforcement

& Audit (OIEA)

Office of Public Health

Science (OPHS)

Office of Policy and Program

Development (OPPD)

Includes Compliance and

Investigations Division (CID)

Includes Recall Management

and Technical Analysis Division (RMTAD)

Includes Applied Epidemiology Staff

(AES), Science Staff (SciS), and Food Safety Laboratories

Includes Congressional &

Public Affairs Staff (CPA)


FSIS Eastern Laboratory and Characterization Branch

Office of Public Health and Science

Eastern Laboratory

Microbiology Characterization

Branch

Microbiology Screening

Branch Chemistry Branch Pathology Branch

Midwestern Laboratory

Western Laboratory

Laboratory Quality

Assurance Staff

Food Emergency Response Staff

Russell Research Center, Athens, GA

Our Branch – Staff of 17

Pulsed-field gel electrophoresis (PFGE)Performed on all isolates (Salmonella, Campylobacter, L.

monocytogenes, E. coli O157:H7, and non-O157 STEC)Analyze images and uploaded to CDC-PulseNet

Salmonella serotypingMolecular serotyping and/or NVSL traditional serotyping

Campylobacter speciationC. jejuni, C. lari, and C. coli

Antimicrobial Resistance ProfilesPerformed on all Salmonella isolates, Campylobacter, STEC,

NARMS Enterococcus and NARMS E. coli isolates

Whole genome sequencing

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Microbial Characterization Tools to Support Food Safety

Food Safety and Inspection Service:

WGS in FSIS: Sources of Bacterial Cultures for WGS

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• PRJNA242847 - GenomeTrakr Project: USDA – FSIS (Salmonella)

• PRJNA215355 - GenomeTrakr Project: FDA (Listeria monocytogenes)

• PRJNA287430 - USDA-FSIS: Isolated strains of Campylobacter spp. genome sequencing

• PRJNA268206 - GenomeTrakr Project: USDA – FSIS Shiga toxin-producing E. coli (STEC)

• PRJNA292666 - FSIS NARMS Salmonella

• PRJNA292667 - FSIS NARMS Escherichia coli

• PRJNA292668 - FSIS NARMS Campylobacter

• PRJNA292669 - FSIS NARMS Enterococcus

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WGS in FSIS: NCBI BioProjects - Data related to a single initiative

4/2014• 2 MiSeqs in EL

7/2014

• Sequence Outbreak Isolates

• FDA uploads FSIS sequences to NCBI

5/2015

• EL uploads directly to NCBI

• 15 Lm isolates

10/2016

• Goal of 100% of FSIS isolates sequenced

• ~7,000 isolates sequenced and uploaded

3/2017• Lab-wide SOP for WGS


Past Efforts: WGS – How did we get here?

Additional Key points: 7 Miseqs, hiring of three Public Health Specialist (Bioinformatics) positions


WGS in FSIS: Timeline to WGS completion

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Sample shipped to Field Service Lab (FSL)

1 day

Characterization (PFGE, AST, serotyping, speciation)

6 days

DNA isolation and quantification

1 day

WGS library prep

1-2 days

Load/Run Miseq2-3 days

WGS data transfer, QC and upload

1 day

Isolation at FSLs4-7 days

Ship sample to MCB-EL

1 day

WGS Timeline - 16 Isolates

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DNA Extraction from clonal source (16 isolates)3 hours

Day 1 cont.

Day 1

DNA Quality Assessment and Quantitation (Qubit): 2-3 hours

Day 2

Quantify DNA and Library Prep: 3 hours

Day 2 cont.

Day 3

MiSeq Run times300 Cycle Kit-26 hours500 Cycle Kit- 36 hours

Day 3-5

Dilution of DNATagmentation, and Indexing: 3 hours

Indexed product cleanup: 2 hours

Day 6 Data Transfer and Analysis3-8 hours

Combine indexed DNA and load into cartridge: 2 hoursDay 3 cont.


WGS in FSIS: WGS Methodology


WGS in FSIS: Post Sequencing Data Analysis and QC

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MiSeq Run Metrics

Cluster Density, Pass Filter, Q30% Illumina Sequence Analysis Viewer (SAV) – Percent Index

and File Size

Assembly

CLC Genomics Ver. 8 de novo Assembly (fasta) SPAdes version 3.7

BioSample and

SRA upload

Create BioSample for each isolate Link FASTQ files & metadata to BioSample Upload to NCBI via Aspera command-line

Quality Trimming

and Coverage

Quality trimming (Trimmomatic) Average quality assessment-CGPipeline Nucleotide balance Pre and post-trim coverages calculated

QualityControl

Python scripts and BLASTdb for organism check BLASTdb for Campylobacter speciation Kraken for detection of contamination

Gene Identification

ResFinder BLASTdb Virulence BLASTdb Escherichia coli/Salmonella serotyping BLASTdb


WGS in FSIS: Downstream analysis tools

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Analysis Tool Source

Identification of Acquired Antibiotic Resistance Genes

• Local ResFinder

• BLASTdb

• http://www.genomicepidemiology.org

Identification STEC Virulence Genes

• VirulenceFinder

• BLASTdb

• stx and eae subtypes

• http://www.genomicepidemiology.org

MLST Identification- 7 Genes • Local BLASTdb • http://www.genomicepidemiology.org

• http://www.pubmlst.org

Identification of Contamination • Kraken • http://ccb.jhu.edu/software/kraken/

Whole Genome MLST • BioNumerics version 7.5/7.6

• CDC-PulseNet

• CDC-PulseNet Calculation Engine and Applied Maths

• http://www.applied-maths.com

SNP Calling and Phylogeny Inference

• Lyve-SET 1.14f

• CFSAN SNP Pipeline

• NCBI Pathogen Detection

• https://github.com/lskatz/lyve-SET

• https://github.com/CFSAN-Biostatistics/snp-pipeline

• http://www.ncbi.nlm.nih.gov/pathogens


WGS in FSIS: WGS Data Flow

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WGS in FSIS: WGS Data Flow

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Product/Source type (Ready to eat product, raw meat/poultry, environmental swab, etc.)

Year sample was collected

State where sample was collected

Subtyping information Salmonella – serovar

Escherichia coli– O-group

Campylobacter – species

Metadata and sequence data is immediately available for upload to NCBI

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WGS in FSIS: Minimal Metadata Shared on NCBI

Methods of further investigating possible relationships between isolates of interest

High Quality SNP analysis

Whole genome MLST

BLAST based detection of genes (AMR genes)

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WGS in FSIS: Tools used for further analysis

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Applications of WGS at FSIS: Lm HarborageFood Safety and Inspection Service

WGS in FSIS: ‘WGS and PFGE’ – Listeria Experience (Ex-1)

WGS and PFGE comparison

High quality SNPs wgMLST

Same PFGE pattern: WGS agreement 0-2 SNPs 0-7 allele differences

Same PFGE pattern: WGS exclusion 34-45 SNPs 29-33 allele differences

Different PFGE: WGS inclusion 0-3 SNPs 0-5 allele differences

WGS and PFGE results are generally in agreement WGS can sometimes exclude isolates from a group

with the same PFGE pattern or include isolates in a group with different PFGE patterns

In the examples below inclusion or exclusion considered all information including sample metadata (establishment, isolation date, etc.)

Note the trend between the SNP and wgMLST approaches

FSIS is also currently exploring use of WGS as a tool to understand potential harborage of L. monocytogenesin establishments

FSIS works collaboratively with FDA in dual-jurisdiction establishments that produce both FDA and FSIS-regulated productsWhen one agency identifies potential harborage through

bacterial characterization of Listeria isolates (PFGE and/or WGS), information is shared to inform a collaborative regulatory response within the establishment

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WGS in FSIS: Potential use in harborage

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WGS in FSIS: Listeria Experience (Ex-3) - hqSNP Analysis

Lyve-SET 1.14f 90% read support, 20x coverage, 5bp flanking, reference: FSIS1505523 (27 contigs).

SNP matrices were produced using CFSAN SNP pipeline. Trees were assembled in MEGA7 using the Maximum Likelihood method based on the Tamura-Nei model with FSIS1605523 (27 contigs) as the reference.

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WGS in FSIS: Listeria Experience (Ex-3) - wgMLST

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WGS in FSIS: Outbreak Investigations

For all outbreak investigations we coordinate with our public health partners (CDC, FDA, state health departments)

FSIS food isolates included in analyses are chosen using epidemiological data from AES, CDC, and state epidemiologists, and based on subtyping information of historical strains from FSIS sampling programs

Analysis methods used include high quality SNP (hqSNP) analysis and whole genome MLST (wgMLST)

Analysis methods used for regulatory decision-making are those developed and agreed upon by our GenFS partners (CDC, FDA, NCBI)

WGS is currently done concurrently with PFGE and other subtyping methods, no decisions have been made solely on WGS


WGS in FSIS: Caveats and Considerations

Currently there are no policies in place for FSIS usage of WGS data to make WGS decisions

How close is close? The is no established metric that states a minimum number of SNPs or allele differences equates to isolates having a common ancestry

Lack of nomenclature, there is no easily communicated WGS “pattern name” that is analogous to PFGE pattern name


How is FSIS addressing caveats and considerations?

Currently working on Federal register notice Will state FSIS’ intent to use WGS regulatory decisions and explain

rationale Will engage stakeholders and allow for comments

FSIS plans to hold a Public Meeting regarding implementation of WGS

FSIS is a member of Interagency Collaboration on Genomics and Food Safety (Gen-FS) Federal partners: CDC, FDA, NCBI and FSIS Agencies with public health and regulatory focus Discussions is ongoing to include USDA-ARS and USDA-APHIS in Gen-FS

Dr. Glenn Tillman

Dr. Mustafa Simmons

Eastern Laboratory Staff

Public Health Partners


Acknowledgements

jamie wasilenko, ph.d. · jamie wasilenko, ph.d. eastern laboratory microbiology characterization...

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